Researchers introduce ReCoPII framework for PII masking datasets
Researchers released ReCoPII, a framework for creating contextual PII masking datasets without relying on single-author assumptions. The method uses ontology-constrained entity relations, generates placeholder sentences, and derives MASK/KEEP labels programmatically. It was tested on Korean documents from six domains, producing ReCoPII-Ko, a corpus with relation, privacy, and annotation labels.
Key points
- ReCoPII avoids single-author bias by modeling entity relations and generating synthetic data with placeholders
- On Korean documents, it achieved 88.42% annotator agreement for KEEP labels vs 26.27% for SPY-style methods
- Fine-tuned detector over-masks at most 5.04% while keeping leakage under 4.18% on human-verified records
Four annotators agreed with 88.42% of ReCoPII’s sampled KEEP labels, compared to 26.27% for SPY-style augmentation on the same sources. Fine-tuning a contextual masking system with a PII detector reduced over-masking to 5.04% while keeping leakage under 4.18%. The detector matched XLM-R-large performance on KDPII with fewer parameters.
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